1. Field of the Invention
The present invention relates to a method and a system for providing or verifying the identification of an individual. More particularly, the present invention relates to a system, consisting of sensors of various modalities (visible and infrared cameras), as well as means for processing data acquired from an individual's face over time, that is capable of fitting a geometric model template of the face to the data according to a mathematical method. In addition, the present invention relates to a mathematical method of processing the fitted model to extract relevant metadata, and storing them in a machine readable format. In addition, the present invention relates to a mathematical method that allows for comparison against other sets of metadata for the purpose of determining their degree of similarity to each other in order to provide or authenticate identification for the individual.
2. Description of the Related Art
It is increasingly important to identify and/or authenticate the presence of authorized personnel and to document non-authorized or suspicious personnel activities near critical installations, and at key points of entry, such as air, land, and sea ports.
Modern identification and authentication systems typically consist of an apparatus incorporating biometric sensors that capture data from the individual subject. The apparatus contains associated circuitry and hardware that process the data according to mathematical algorithms. The resulting data in a computer readable form is then stored (in the enrollment modality) or compared (in the identification/authentication modality) against a database of stored data. In a secured access environment, a positive match allows access to the facility.
Face recognition is an appealing biometric in that, unlike fingerprints and retina scans, it can be done passively and unobtrusively at a comfortable distance. Unfortunately, when currently available automated face recognition systems are employed in access control applications (cooperative subjects), their performance is suboptimal, and the accuracy degrades significantly when these systems are employed in surveillance (uncooperative subjects). Experiments conducted using the FERET database and during the FRVT 2002 study indicate that the success rate is not sufficient.
These 2D systems, even though they employ sophisticated models and are commercially available, can not overcome the inherent limitations of their input data and suffer from a number of flaws which hamper their accuracy. These flaws manifest themselves under the following conditions: F1) variable face poses, F2) variable face expressions, F3) variable lighting conditions, F4) accessories (e.g., glasses), F5) facial hair, F6) aging, F7) low image quality, and F8) disguises.
The main reason behind these flaws is the input and storage modality of these systems (generally 2D images) which, no matter how advanced the processing that follows, suffer from a lack of adequate basic information. A few recent attempts at other modalities (hybrid/3D data) have been lacking the proper processing algorithms that can take advantage of these more complex data forms.